Multi‐Objective Optimization of a Spherical Thermal Storage Tank Using a Student Psychology‐Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Energy storage technologies often store heat, with water as a preferred medium due to its availability and low cost. However, maintaining water in a liquid state at high temperatures requires large pressure vessels, posing significant design challenges. Balancing thermal storage capacity with pressure constraints is essential. This paper explores the dynamics of thermal storage water tanks, aiming to optimize their design using a multi‐criteria approach. An equilibrium thermodynamic model was developed to evaluate the impact of geometric structure and operating parameters. The results show that optimizing a single variable is insufficient to minimize pressure swing, reduce heat loss, and maximize storage capacity. To address these trade‐offs, a multi‐objective student psychology‐based optimization (SPBO) algorithm was employed for three‐objective optimization, outperforming particle swarm optimization (PSO) in convergence. The technique for order preference by similarity to ideal solution (TOPSIS) method was applied to the Pareto frontier, yielding ideal solutions using both data‐driven and manually weighted approaches. Compared with the initial design, the data‐driven weighted (entropy‐weighted and coefficient of variation methods) optimal designs improved all objectives, reducing pressure swing by 12.8% and 19.8%, respectively. A manually weighted approach reduced pressure swing by up to 86.7%, albeit with a decrease in thermal storage capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it